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The paper "PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation" use RANSAC based voting for localizing keypoints, and further use PnP to calculate object poses.

The paper "6DoF Object Pose Estimation via Differentiable Proxy Voting Regularizer" employ Hough voting in testing to localize keypoints and then EPnP to solve 6DOF poses. It has been mentioned in "Implementation details" section of their paper. I'm not sure What do they employ in training to localize keypoints, whether Hough voting or RANSAC??

What's the difference b/w RANSAC-based voting & Hough voting to estimate vector-field for keypoints localization? Which one is better in which situations?

ML Dev
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  • Hough voting and RANSAC are quite different in nature. I'm not sure SO is the right place for this answer – Shai May 03 '21 at 09:11
  • @Shai, I'm not asking the difference between Hough voting and RANSAC. My question is very much clear and its about using these two for estimating keypoints in semantically segmented objects using vector-field. – ML Dev May 09 '21 at 10:16

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